openai/swarm

Features

• Agent Delegation - Enables modular task delegation by allowing agents to transfer execution to specialized agents.
• Multi-Agent Orchestrators - Coordinates teams of specialized agents that collaborate and hand off tasks to one another to complete complex workflows.
• Agentic Loops - Processes agent responses and function outputs sequentially in a central control loop.
• Conversational Workflow Engines - Manages persistent, multi-turn dialogues by dynamically delegating tasks between agents and maintaining session context.
• Multi-Agent Routing Systems - Directs user requests to specialized agents based on intent using a central triage agent.
• Conversational State Managers - Maintains persistent user context and shared variables across multi-turn dialogues.
• Multi-Agent Orchestration Frameworks - Coordinates handoffs and shared state between specialized agents to execute complex, multi-step LLM workflows.
• Multi-Agent Coordination Systems - Provides a toolkit for building conversational systems that route tasks between specialized agents and execute local functions.
• Agent Orchestration Loops - Sustains continuous communication cycles between users and agents to facilitate ongoing dialogue.
• AI Agent Capabilities - Provides functional extensions that allow agents to execute custom code and retrieve data during conversations.
• External Tool Integration - Supports the invocation of custom external tools to retrieve real-time data or perform actions.
• Function Orchestrators - Resolves structured function calls returned by agents to execute local code within the conversation loop.
• LLM Tool Calling - Maps natural language intents to executable local functions and external tools within multi-agent workflows.
• Agent State Persistence - Maintains a persistent dictionary of variables passed between agents to preserve session state.
• State and Context Management - Passes and updates shared state across agent interactions to ensure consistent data availability.
• Context Persistence - Maintains persistent variables and conversation state across multi-turn interactions.
• Session Management Systems - Executes persistent loops that allow users to engage with multi-agent systems through a conversational interface.
• Agent Frameworks - Lightweight multi-agent orchestration framework.
• Agent Orchestration - Educational framework for lightweight multi-agent orchestration.
• Agentic Frameworks - Lightweight framework for coordinating multi-agent orchestration and handoffs.
• AI Agent Frameworks - Educational framework for multi-agent orchestration.
• Multi-Agent Systems - Lightweight framework for orchestrating multi-agent workflows.
• Agentic AI - Listed in the “Agentic AI” section of the The Incredible Pytorch awesome list.
• External Data Connectors - Connects agent logic to persistent databases to retrieve and update records during task execution.
• Schema-Driven Specifications - Maps external capabilities to executable functions using schema-driven definitions.
• Agent Streaming Interfaces - Streams incremental updates and control signals to provide visibility into agent switches and task execution.
• Agent Response Streamers - Streams agent responses and function execution events incrementally to provide real-time visibility into task progress.
• Technical Documentation Retrieval - Queries vector databases to fetch relevant information for support responses during agent interactions.
• Agent-Integrated Functions - Enables agents to invoke local code directly to perform data transformations or external actions.
• Incremental Streaming - Emits real-time updates and control signals during agent task execution for workflow visibility.
• Agent Evaluation Frameworks - Validates agent decision-making by running automated test cases against conversation inputs to verify function calls and task execution.

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